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(A) Streamlined Protocol
Abstract
High Throughput Single-stranded Plasma DNA Sequencing Using
Thermostable Group II Intron Reverse Transcriptase
Douglas C. Wu and Alan M. Lambowitz
Institute of Cellular and Molecular Biology and Department of Molecular Biosciences, The University of Texas at Austin
(B) Recovers Broad Size
Range of DNA fragments
(C) Nucleosome Positionings
(D) Cells origin inferred by
transcription factor footprinting
Conclusion
•TGIRTs enable efficient ssDNA-seq that can be
use for analysis of cfDNA in human plasma and
other bodily fluids.
•Identification of protein binding features of cfDNA
that provide information about the tissue of origin
and have potential diagnostic applications
•Enables effective library construction from
degraded samples, bisulfite-treated DNA and
ancient DNA
Reference
1. Snyder et al., Cell 2016
2. Sun et al., PNAS 2015
3. Butler et al., PLoS One 2015
NIH grants GM37949 and GM37951 and Welch Foundation Grant F-1607
Thermostable group II intron reverse transcriptase (TGIRT) enzymes and
methods for their use are the subject of patents and patent applications that
have been licensed by the University of Texas at Austin and East Tennessee
State University to InGex, LLC. A.M.L. and the University of Texas are
minority equity holders in InGex, LLC, and A.M. L. and other present and
former Lambowitz laboratory members receive royalty payments from sales of
TGIRT enzymes and licensing of intellectual property.
High-throughput DNA sequencing (DNA-seq) of cell-free DNA (cfDNA) in
plasma and other bodily fluids is a powerful method for non-invasive
prenatal testing and diagnosis (i.e., liquid biopsy) of cancer and other
diseases. In healthy individuals, cfDNA in human plasma consists largely
of ~160-bp DNA fragments derived from nucleosomes released by
apoptosis of lymphoid and myeloid cells in blood. By contrast, in a
variety of pathological conditions, plasma is enriched in DNA fragments
released from dying cells in the affected tissues and can be identified by
tissue-specific differences in nucleosome spacing, transcription factor
(TF) occupancy1, and DNA methylation sites2, thereby providing
diagnostic information. In cancer patients, a significant proportion of
cfDNA originates from tumor cells and retains epigenetic features of the
tumor tissue type (3-93% in one study depending on the type of cancer)3.
These features of cfDNAs are best analyzed by single-stranded DNA
sequence (ssDNA-seq), which is better suited for the analysis of
fragmented and nicked DNA than are conventional dsDNA-seq methods1.
However, established methods for ssDNA-seq, which were originally
developed for the analysis of ancient DNA, are inefficient, costly and
time-consuming4. Here, we present an efficient method for ssDNA-seq,
which takes advantage of the beneficial properties of thermostable group
II intron reverse transcriptases (TGIRTs) and the use of this method for
the analysis of cfDNA in human plasma. By exploiting a novel template-
switching activity of TGIRTs to add DNA-seq adaptors, DNA-seq libraries
can be constructed from small amounts of starting material in <2 h. In
future research, we will use this method for the analysis of clinical
samples to develop new cfDNA-based diagnostic procedures.
4. Gansauge and Meyer, Nat Prot 2013
5. Thurman et al., Nature 2012
6. Qin et al., RNA 2016
(3) Template-switching by TGIRT
Alkaline treatment
cDNA clean-up
(4) Adaptor ligation by
thermostable 5’ AppDNA/RNA ligase
R2 RNA
3’-Blocker
5’
5’
3’-N
R2R DNA
5’ 3’OH
TGIRT
cDNA clean-up
(5) PCR amplification
5’-App3’-Blocker 5’3’
R1R R2R
5’
3’
R2R
R2P5
3’
5’
Barcode+P7
R1R
R1
’
5’
5’P
DNA nick
P
(2) Dephosphorylation&
denaturation
5’ 3’ OH
5’3’ OH
(1) Plasma DNA
Target DNA (-)
5’
5’
5’ 3’ OH
3’ OH
3’ OH5’ 3’ OH
5’ 3’ OH
Target DNA (-)
Target DNA (+)
Target DNA (+)
Target DNA (-)
20 min
20 min
60 min
~2 ng
Grant Support and
Conflict-of-interest statement
ssDNA−seq
(ref 1)
TGIRT−seq
0.00 0.25 0.50 0.75 1.00
Blood
Blood vessel
Heart
Kidney
Lung
Muscle
Prostate
ssDNA−seq (ref 1) TGIRT−seq
0.0
0.5
1.0
1.5
50
100
150
200
250
300
350
400 50
100
150
200
250
300
350
400
Insert Size (bp)
Percentreads
−2e+05
0e+00
2e+05
−50000
0
50000
Long(120−180bp)Short(35−80bp)
−1000−800−600−400−200
02004006008001000
−20000
0
20000
40000
−2000
0
2000
4000
Long(120−180bp)Short(35−80bp)
−1000−800−600−400−200
02004006008001000
Distance to CTCF start site (bp)
AdjustedWPS
0.000
0.002
0.004
0.006
−500
−450
−400
−350
−300
−250
−200
−150
−100
−50
0
50
100
150
200
250
300
350
400
450
500Distance to the Nearest Peak (bp)
[ssDNA−seq (ref 1) & TGIRT−seq]
Desnity
0.000
0.005
0.010
0.015
100 200 300 400 500
Distance to the Nearest Peak (bp)
FractionofPeaks
ssDNA−seq (ref 1)
TGIRT−seq

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Use of Thermostable Group II Intron Reverse Transcriptases (TGIRTs) for Single-Stranded DNA-seq of Cell-Free DNA in Human Plasma

  • 1. (A) Streamlined Protocol Abstract High Throughput Single-stranded Plasma DNA Sequencing Using Thermostable Group II Intron Reverse Transcriptase Douglas C. Wu and Alan M. Lambowitz Institute of Cellular and Molecular Biology and Department of Molecular Biosciences, The University of Texas at Austin (B) Recovers Broad Size Range of DNA fragments (C) Nucleosome Positionings (D) Cells origin inferred by transcription factor footprinting Conclusion •TGIRTs enable efficient ssDNA-seq that can be use for analysis of cfDNA in human plasma and other bodily fluids. •Identification of protein binding features of cfDNA that provide information about the tissue of origin and have potential diagnostic applications •Enables effective library construction from degraded samples, bisulfite-treated DNA and ancient DNA Reference 1. Snyder et al., Cell 2016 2. Sun et al., PNAS 2015 3. Butler et al., PLoS One 2015 NIH grants GM37949 and GM37951 and Welch Foundation Grant F-1607 Thermostable group II intron reverse transcriptase (TGIRT) enzymes and methods for their use are the subject of patents and patent applications that have been licensed by the University of Texas at Austin and East Tennessee State University to InGex, LLC. A.M.L. and the University of Texas are minority equity holders in InGex, LLC, and A.M. L. and other present and former Lambowitz laboratory members receive royalty payments from sales of TGIRT enzymes and licensing of intellectual property. High-throughput DNA sequencing (DNA-seq) of cell-free DNA (cfDNA) in plasma and other bodily fluids is a powerful method for non-invasive prenatal testing and diagnosis (i.e., liquid biopsy) of cancer and other diseases. In healthy individuals, cfDNA in human plasma consists largely of ~160-bp DNA fragments derived from nucleosomes released by apoptosis of lymphoid and myeloid cells in blood. By contrast, in a variety of pathological conditions, plasma is enriched in DNA fragments released from dying cells in the affected tissues and can be identified by tissue-specific differences in nucleosome spacing, transcription factor (TF) occupancy1, and DNA methylation sites2, thereby providing diagnostic information. In cancer patients, a significant proportion of cfDNA originates from tumor cells and retains epigenetic features of the tumor tissue type (3-93% in one study depending on the type of cancer)3. These features of cfDNAs are best analyzed by single-stranded DNA sequence (ssDNA-seq), which is better suited for the analysis of fragmented and nicked DNA than are conventional dsDNA-seq methods1. However, established methods for ssDNA-seq, which were originally developed for the analysis of ancient DNA, are inefficient, costly and time-consuming4. Here, we present an efficient method for ssDNA-seq, which takes advantage of the beneficial properties of thermostable group II intron reverse transcriptases (TGIRTs) and the use of this method for the analysis of cfDNA in human plasma. By exploiting a novel template- switching activity of TGIRTs to add DNA-seq adaptors, DNA-seq libraries can be constructed from small amounts of starting material in <2 h. In future research, we will use this method for the analysis of clinical samples to develop new cfDNA-based diagnostic procedures. 4. Gansauge and Meyer, Nat Prot 2013 5. Thurman et al., Nature 2012 6. Qin et al., RNA 2016 (3) Template-switching by TGIRT Alkaline treatment cDNA clean-up (4) Adaptor ligation by thermostable 5’ AppDNA/RNA ligase R2 RNA 3’-Blocker 5’ 5’ 3’-N R2R DNA 5’ 3’OH TGIRT cDNA clean-up (5) PCR amplification 5’-App3’-Blocker 5’3’ R1R R2R 5’ 3’ R2R R2P5 3’ 5’ Barcode+P7 R1R R1 ’ 5’ 5’P DNA nick P (2) Dephosphorylation& denaturation 5’ 3’ OH 5’3’ OH (1) Plasma DNA Target DNA (-) 5’ 5’ 5’ 3’ OH 3’ OH 3’ OH5’ 3’ OH 5’ 3’ OH Target DNA (-) Target DNA (+) Target DNA (+) Target DNA (-) 20 min 20 min 60 min ~2 ng Grant Support and Conflict-of-interest statement ssDNA−seq (ref 1) TGIRT−seq 0.00 0.25 0.50 0.75 1.00 Blood Blood vessel Heart Kidney Lung Muscle Prostate ssDNA−seq (ref 1) TGIRT−seq 0.0 0.5 1.0 1.5 50 100 150 200 250 300 350 400 50 100 150 200 250 300 350 400 Insert Size (bp) Percentreads −2e+05 0e+00 2e+05 −50000 0 50000 Long(120−180bp)Short(35−80bp) −1000−800−600−400−200 02004006008001000 −20000 0 20000 40000 −2000 0 2000 4000 Long(120−180bp)Short(35−80bp) −1000−800−600−400−200 02004006008001000 Distance to CTCF start site (bp) AdjustedWPS 0.000 0.002 0.004 0.006 −500 −450 −400 −350 −300 −250 −200 −150 −100 −50 0 50 100 150 200 250 300 350 400 450 500Distance to the Nearest Peak (bp) [ssDNA−seq (ref 1) & TGIRT−seq] Desnity 0.000 0.005 0.010 0.015 100 200 300 400 500 Distance to the Nearest Peak (bp) FractionofPeaks ssDNA−seq (ref 1) TGIRT−seq